Groundwater nitrate response to sustainable nitrogen management.
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ABSTRACT:
SUBMITTER: Hansen B
PROVIDER: S-EPMC5561247 | biostudies-other | 2017 Aug
REPOSITORIES: biostudies-other
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Project description:The predicting groundwater nitrate pollution risk, especially in terms of changes in fertilizing, has not been fully investigated so far. In particular, there is no comprehensive method to assess this risk in areas of different land use type, and not only in agricultural areas. The aim of this study was to develop a novel multicriteria methodology for groundwater nitrate pollution risk assessment, which meets these issues. A further aim was to determine how much this risk would change if the amount of organic and synthetic fertilization was reduced. An assumption was that groundwater pollution risk is a combination of the potential adverse impacts of land use, fertilization, and intrinsic groundwater vulnerability to pollution. The impact of fertilization was holistically evaluated by balancing nitrogen from spatially differentiated the size of the breeding, species of livestock, manure and synthetic fertilizers input, and spatially differentiated topsoil, with nitrogen uptake by different crops. The nitrate concentration in the leachate was used as a measure of the impact of fertilization. This concentration was compared to the natural baseline nitrate concentration in groundwater. Three fertilization scenarios for groundwater pollution risk assessment in two study areas were discussed. Under typical agricultural, climatic, soil, and geological conditions in Europe for the current total fertilization level of 95-120 kg N ha-1 groundwater nitrate pollution risk is low and moderate, but for fertilization of 150-180 kg N ha-1, a reduction in the total fertilization (synthetic and manure) by 40 to 50% may be required to achieve low risk of degradation of natural groundwater quality. Predictive simulations of groundwater nitrate pollution risk confirmed that reducing synthetic and organic fertilization has an effect, especially in areas with intensive fertilization. This method may allow for a holistic and scenario-based assessment of groundwater pollution risk and may help decision-makers introduce solutions to manage this risk under conditions of climate change, preservation of groundwater quality, and food security.
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Project description:The tremendous increase in industrial development and urbanization has become a severe threat to the Chinese climate and food security. The Agricultural Production System Simulator model was used to simulate soil nitrogen in black soil in Yangling Jilin Province for 20 years. The observed values are consistent with the simulated values. The predicted values of total soil NO3--N and NH4+-N nitrogen are 10 kg ha-1 and 5 kg ha-1 higher than the observed values. The total soil NO3--N loss has the same trend as the rainfall, and it increases with the number of rainfall days over the years. The average 20 years losses of NO3--N and NH4+-N observed were 1375.91 kg ha-1, and 9.24 kg ha-1, while in the simulation increase was 1387.01 kg ha-1 and 9.28 kg ha-1, respectively. The difference between the observed and simulated values of NO3--N and NH4+-N of mean loss was 11.15 kg ha-1 and 0.04 kg ha-1 respectively. Moreover, our findings highlight the opportunity further to improve management policies (especially for nitrogen) to maintain crop yield.
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Project description:Biogeochemical reactions occur unevenly in space and time, but this heterogeneity is often simplified as a linear average due to sparse data, especially in subsurface environments where access is limited. For example, little is known about the spatial variability of groundwater denitrification, an important process in removing nitrate originating from agriculture and land use conversion. Information about the rate, arrangement, and extent of denitrification is needed to determine sustainable limits of human activity and to predict recovery time frames. Here, we developed and validated a method for inferring the spatial organization of sequential biogeochemical reactions in an aquifer in France. We applied it to five other aquifers in different geological settings located in the United States and compared results among 44 locations across the six aquifers to assess the generality of reactivity trends. Of the sampling locations, 79% showed pronounced increases of reactivity with depth. This suggests that previous estimates of denitrification have underestimated the capacity of deep aquifers to remove nitrate, while overestimating nitrate removal in shallow flow paths. Oxygen and nitrate reduction likely increases with depth because there is relatively little organic carbon in agricultural soils and because excess nitrate input has depleted solid phase electron donors near the surface. Our findings explain the long-standing conundrum of why apparent reaction rates of oxygen in aquifers are typically smaller than those of nitrate, which is energetically less favorable. This stratified reactivity framework is promising for mapping vertical reactivity trends in aquifers, generating new understanding of subsurface ecosystems and their capacity to remove contaminants.
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Project description:Contamination of drinking water by nitrate is a growing problem in many agricultural areas of the country. Ingested nitrate can lead to the endogenous formation of N-nitroso compounds, potent carcinogens. We developed a predictive model for nitrate concentrations in private wells in Iowa. Using 34,084 measurements of nitrate in private wells, we trained and tested random forest models to predict log nitrate levels by systematically assessing the predictive performance of 179 variables in 36 thematic groups (well depth, distance to sinkholes, location, land use, soil characteristics, nitrogen inputs, meteorology, and other factors). The final model contained 66 variables in 17 groups. Some of the most important variables were well depth, slope length within 1 km of the well, year of sample, and distance to nearest animal feeding operation. The correlation between observed and estimated nitrate concentrations was excellent in the training set (r-square=0.77) and was acceptable in the testing set (r-square=0.38). The random forest model had substantially better predictive performance than a traditional linear regression model or a regression tree. Our model will be used to investigate the association between nitrate levels in drinking water and cancer risk in the Iowa participants of the Agricultural Health Study cohort.
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Project description:Insight into the response of cereal yields to nitrogen fertilizer is fundamental to improving nutrient management and policies to sustain economic crop benefits and food sufficiency with minimum nitrogen pollution. Here we propose a new method to assess long-term (LT) regional sustainable nitrogen inputs. The core is a novel scaled response function between normalized yield and total net nitrogen input. The function was derived from 25 LT field trials for wheat, maize and barley in Europe, Asia and North America and is fitted by a second-order polynomial (R2 = 0.82). Using response functions derived from common short-term field trials, with soil nitrogen not in steady state, gives the risks of soil nitrogen depletion or nitrogen pollution. The scaled LT curve implies that the total nitrogen input required to attain the maximum yield is independent of this maximum yield as postulated by Mitscherlich in 1924. This unique curve was incorporated into a simple economic model with valuation of externalities of nitrogen surplus as a function of regional per-capita gross domestic product. The resulting LT sustainable nitrogen inputs range from 150 to 200 kgN ha-1 and this interval narrows with increasing yield potential and decreasing gross domestic product. The adoption of LT response curves and external costs in cereals may have important implications for policies and application ceilings for nitrogen use in regional and global agriculture and ultimately the global distribution of cereal production.
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Project description:BackgroundClimate change produces extremes in both temperature and precipitation causing increased drought severity and increased reliance on groundwater resources. Agricultural practices, which rely on groundwater, are sensitive to but also sources of contaminants, including nitrate. How agricultural contamination drives groundwater geochemistry through microbial metabolism is poorly understood.MethodsOn an active cow dairy in the Central Valley of California, we sampled groundwater from three wells at depths of 4.3 m (two wells) and 100 m (one well) below ground surface (bgs) as well as an effluent surface water lagoon that fertilizes surrounding corn fields. We analyzed the samples for concentrations of solutes, heavy metals, and USDA pathogenic bacteria of the Escherichia coli and Enterococcus groups as part of a long term groundwater monitoring study. Whole metagenome shotgun sequencing and assembly revealed taxonomic composition and metabolic potential of the community.ResultsElevated nitrate and dissolved organic carbon occurred at 4.3m but not at 100m bgs. Metagenomics confirmed chemical observations and revealed several Planctomycete genomes, including a new Brocadiaceae lineage and a likely Planctomycetes OM190, as well novel diversity and high abundance of nano-prokaryotes from the Candidate Phyla Radiation (CPR), the Diapherotrites, Parvarchaeota, Aenigmarchaeota, Nanoarchaeota, Nanohaloarchaea (DPANN) and the Thaumarchaeota, Aigarchaeota, Crenarchaeota, Korarchaeota (TACK) superphyla. Pathway analysis suggests community interactions based on complimentary primary metabolic pathways and abundant secondary metabolite operons encoding antimicrobials and quorum sensing systems.ConclusionsThe metagenomes show strong resemblance to activated sludge communities from a nitrogen removal reactor at a wastewater treatment plant, suggesting that natural bioremediation occurs through microbial metabolism. Elevated nitrate and rich secondary metabolite biosynthetic capacity suggest incomplete remediation and the potential for novel pharmacologically active compounds.
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